Synthetic Lethal Networks Research Articles

Abstract 3118: Predicting response to PARP inhibitors in pediatric cancer via synthetic lethal networks

Abstract Introduction: PARP inhibitors (PARPi) are known to have a synthetic lethal (SL) relationship with BRCA1/2 mutated cancers (ie: BRCA loss confers sensitivity to PARPi), but heterogeneity in response exists. PARPi have also found utility in BRCA-non mutated cancers, suggesting other factors may contribute to response. Recently, PARPi trials in pediatric solid tumors have had mixed results. SL interactions have been shown to exist in a network relationship, as such the aim of this study was to identify a clinically relevant SL signature (including synthetic dosage lethality and synthetic rescue) that would predict response to PARPi in pediatric cancers. Methods: Pre-treatment genomic copy number alteration, RNA sequencing, and survival data from 519 pediatric cancer patients (INFORM Consortium, 91% solid tumor) were obtained. SL partner genes for PARPi were selected if co-alteration of the drug target (PARP1/2/3) and candidate gene was 1) found to be under-represented in the patient transcriptome, 2) associated with improved survival, and 3) the genes were phylogenetically similar to PARP1/2/3. The 100 gene signature was validated on two independent pediatric cancer drug screens: in-vivo orthotopic patient-derived xenograft (OPDX) mouse models (n=24) and in-vitro PRISM cell lines (n=54). It was also validated on a cisplatin (PARP surrogate) treated pediatric osteosarcoma clinical trial (n=37). Comparisons were made to 1) a signature generated with the same pipeline using adult TCGA data, 2) a validated adult synthetic lethality pipeline (SELECT), and 3) a published 60-gene PARPi response signature (BRCAness score). Results: The pediatric signature predicted response in all drug screens evaluated. Four cell line screens: Talazoparib (AUC 0.72, p = 0.003), Rucaparib (AUC 0.69, p = 0.01), Niraparib (AUC 0.67, p = 0.03), Olaparib (AUC 0.64, p = 0.06), and two OPDX screens: Talazoparib (AUC 0.74, p = 0.03), Olaparib (AUC 0.74, p = 0.03). The pediatric signature outperformed both adult signatures (AUCs 0.34-0.61, all p’s >0.05), and the BRCAness score (AUCs 0.41-0.53, p’s >0.05) except in the Olaparib cell line (BRCAness: AUC 0.68, p = 0.01). In the pediatric osteosarcoma dataset, the pediatric signature predicted death (AUC 0.68) and two-year recurrence (AUC 0.72), outperforming the other measures (death: AUC 0.49-0.66; two-year recurrence: AUCs 0.36-0.44). Discussion: We developed a pediatric derived, clinically relevant, synthetic lethal signature that predicts response to PARPi in pan-pediatric cancer drug screens and in a cisplatin treated osteosarcoma clinical trial. Our findings support the goal of identifying precision oncology frameworks to improve the care of children with cancer. Citation Format: Matthew Nagy, Fiorella Schischlik, Kun Wang, Nishanth Ulhas Nair, E. Michael Gertz, Lipika R. Pal, Natalie Jaeger, Christopher Previti, Dina ElHarouni, Stefan M. Pfister, Eytan Ruppin. Predicting response to PARP inhibitors in pediatric cancer via synthetic lethal networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3118.

Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma

The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

P53-Mediated Indirect Regulation on Cellular Metabolism: From the Mechanism of Pathogenesis to the Development of Cancer Therapeutics.

The transcription factor p53 is the most well-characterized tumor suppressor involved in multiple cellular processes, which has expanded to the regulation of metabolism in recent decades. Accumulating evidence reinforces the link between the disturbance of p53-relevant metabolic activities and tumor development. However, a full-fledged understanding of the metabolic roles of p53 and the underlying detailed molecular mechanisms in human normal and cancer cells remain elusive, and persistent endeavor is required to foster the entry of drugs targeting p53 into clinical use. This mini-review summarizes the indirect regulation of cellular metabolism by wild-type p53 as well as mutant p53, in which mechanisms are categorized into three major groups: through modulating downstream transcriptional targets, protein-protein interaction with other transcription factors, and affecting signaling pathways. Indirect mechanisms expand the p53 regulatory networks of cellular metabolism, making p53 a master regulator of metabolism and a key metabolic sensor. Moreover, we provide a brief overview of recent achievements and potential developments in the therapeutic strategies targeting mutant p53, emphasizing synthetic lethal methods targeting mutant p53 with metabolism. Then, we delineate synthetic lethality targeting mutant p53 with its indirect regulation on metabolism, which expands the synthetic lethal networks of mutant p53 and broadens the horizon of developing novel therapeutic strategies for p53 mutated cancers, providing more opportunities for cancer patients with mutant p53. Finally, the limitations and current research gaps in studies of metabolic networks controlled by p53 and challenges of research on p53-mediated indirect regulation on metabolism are further discussed.

Rapid Inhibitor Discovery by Exploiting Synthetic Lethality.

Synthetic lethality occurs when inactivation of two genes is lethal but inactivation of either single gene is not. This phenomenon provides an opportunity for efficient compound discovery. Using differential growth screens, one can identify biologically active compounds that selectively inhibit proteins within the synthetic lethal network of any inactivated gene. Here, based purely on synthetic lethalities, we identified two compounds as the only possible inhibitors of Staphylococcus aureus lipoteichoic acid (LTA) biosynthesis from a screen of ∼230,000 compounds. Both compounds proved to inhibit the glycosyltransferase UgtP, which assembles the LTA glycolipid anchor. UgtP is required for β-lactam resistance in methicillin-resistant S. aureus (MRSA), and the inhibitors restored sensitivity to oxacillin in a highly resistant S. aureus strain. As no other compounds were pursued as possible LTA glycolipid assembly inhibitors, this work demonstrates the extraordinary efficiency of screens that exploit synthetic lethality to discover compounds that target specified pathways. The general approach should be applicable not only to other bacteria but also to eukaryotic cells.

Abstract NG02: A network of deeply conserved synthetic-lethal interactions for exploration of precision cancer therapy

Abstract NG02: A network of deeply conserved synthetic-lethal interactions for exploration of precision cancer therapy

Abstract A32: Systematic reconstruction of the cancer synthetic lethal network and its application for the identification of selective cancer drug targets

Abstract Motivation: Synthetic Lethal (SL) interactions can provide a basis for the development of personalized anticancer drugs with higher therapeutic efficacy and reduced toxicity. Emerging screening technologies have enabled the detection of SL interactions in human cell lines in recent years. However, such screens are confined to find the SL-partners of a small set of genes or drugs, and yet cannot encompass the large spectrum of cancer types and genetic interactions. As these screens are mostly conducted in cancer cell lines or in animal models, they may fail to capture the SL interactions that occur in human cancers in-vivo. As a result, the promise of harnessing SL interactions to develop selective anticancer drugs is yet to be fully explored and exploited. Methods: Here we present a novel computational framework for the genome-scale identification of SL interactions in cancer, which spans different cancer types in a clinical setting. Our approach stems from the basic assertion that cancer cells that have lost two SL-paired genes are strongly selected against. This observation enables us to identify SL interactions by analyzing the genetic profiles of thousands of clinical samples and detecting events of gene-co-deletions that occur significantly less than expected. Based on converging evidence from such an analysis of different genome-scale cancer datasets we have assembled a core set of SL interactions that are supported by at least three different datasets. These SLs are shared by the wide range of cancer types in a statistically enriched manner, and constitute the Cancer Synthetic Lethal Network (CSLN). Results: Testing the veracity of the CSLN, we employed it to infer gene essentiality in 26 different cancer cell lines, based on their genetic profiles. The obtained cell line-specific gene essentiality predictions significantly match pertaining experimental essentiality measures (Marcotte et al., Cancer Discovery, 2011). Remarkably, the significant predictive power of the CSLN surpassed that obtained when using a cohort of SL-interactions detected experimentally. Following, we comprehensively tabulated the therapeutic potential of targeting the CSLN genes in different cancer types, to provide an array of novel cancer-specific drug target and repurposing predictions. Extending the concept of synthetic lethality to target oncogenes that are difficult to target directly, we identified synthetic lethality arising from gene amplification, rather than deletion, and constructed the Cancer Amplification SL Network (CASLN). We employed it to predict the efficacy of 30 different drugs across 593 cancer cell lines, which have been recently tested experimentally (Garnett et al., Nature, 2012). Indeed, drugs were significantly more effective in the predicted sensitive cell lines than in those predicted insensitive. Significance: This study paves the way for obtaining and exploiting a system-level understanding of SL interactions in cancer in three important ways: 1. It provides a novel computational pipeline for detecting SL interactions in cancer. An approach that can also be employed to detect context-dependent SL interactions that are specific to certain cancer types (as we demonstrate by generating and testing a breast cancer SL network). 2. It presents two large-scale networks of genetic interactions in cancer, whose utility is manifested by their ability to predict gene essentiality and drug efficacy in a cell-line-specific manner. 3. The broad set of novel cancer-specific drug target predictions obtained is likely to provide a basis for numerous follow-up experimental studies. In summary, our approach opens up exciting opportunities for rational drug design and for the development of personalized therapeutic strategies for cancer. Citation Format: Livnat Jerby Arnon, Adam Weinstock, Tamar Geiger, Eytan Ruppin. Systematic reconstruction of the cancer synthetic lethal network and its application for the identification of selective cancer drug targets. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr A32.

Abstract PR05: Construction of synthetic lethal networks for p53 tumor suppressor pathways identifies candidate therapeutic targets for metastatic, chemotherapy resistant HNSCC

Abstract p53 deficient cells are resistant to apoptosis, which contributes to malignant progression and therapy resistance of p53 mutant tumors. For this reason, identifying and targeting p53 independent cell death pathways is an important therapeutic goal. We previously reported that the DNA damage kinases Atm and DNA-PK exhibit p53 independent synthetic lethal interactions (Current Biology 11:191-194, 2001) and that DNA-PK regulates a DNA damage induced apoptotic response that is p53 independent (EMBO Reports, 10:87-93, 2009). To identify additional synthetic lethal interactions with the p53 pathway, we used high throughput siRNA screens to interrogate the kinomes of both human and mouse tumor cell cultures stratified for p53 status and metastatic potential. Mouse cells included low passage squamous cell carcinoma (SCC) cells isolated from tumors from five genotypes of mice: wild type, Atm-/-, DNA-PKcs-/-, p19/Arf -/- and p53+/-. Human cells were derived from both primary and metastatic head and neck squamous cell carcinomas (HNSCC). siRNA kinome screens were run in the presence of three concentrations of the chemotherapy agent doxorubicin to identify genotype specific sensitizers. We identified numerous additional synthetic partners with each p53 pathway mutation: Atm, DNA-PKcs, p19/Arf, and p53. These synthetic lethal interactions varied with doxorubicin exposure, thus defining gene targets that sensitize tumor cells of defined genotype to genotoxic therapy. Cross species comparison and independent validation was used to prioritize 20 survival kinases specific to p53 mutant metastatic HNSCC. These kinases are involved in phosphatidylinositol, Src, focal adhesion and integrin signaling pathways, and G2/M cell cycle transition. We selected the G2/M regulatory kinase, WEE1, for preclinical testing as a small molecule inhibitor is available with potential for clinical application. Relative to p53 wild type SCC cells, p53 deficient cells were more sensitive to the WEE1 inhibitor MK-1775, as well as the CHK1 inhibitor AZD7762 providing chemical confirmation of our siRNA screen results. Examination of 42 additional SCC cell lines revealed that p53 mutation conferred increased sensitivity to a dual WEE1/CHK1 inhibitor. Treatment of mice bearing xenografts of p53 mutant HNSCC cells with MK-1775, either alone or in conjunction with cisplatin, impaired tumor growth and in some cases led to tumor regression. These results demonstrate the utility of high throughput siRNA screens applied to genetically defined early passage tumor cells combined with cross species comparisons to efficiently pinpoint novel drug targets for p53 mutant cancers. This abstract is also presented as Poster A04. Citation Format: Russell Moser, Chang Xu, Michael Kao, In Sock Jang, Kay Gurley, Adam Margolin, Carla Grandori, Eddie Mendez, Chris Kemp. Construction of synthetic lethal networks for p53 tumor suppressor pathways identifies candidate therapeutic targets for metastatic, chemotherapy resistant HNSCC. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR05.

The functional relationships underlying a synthetic genetic network

Here, we focus on synthetic lethal genetic interactions, examples of genetic enhancements, where mutations in two different genes result in lethality but only when present together. We recently identified the synthetic lethal network around the PKC1 gene encoding the essential protein kinase C of yeast. We found that this network is heavily enriched for interactions with genes whose products are closely linked to Pkc1 signallng in vivo. Here, we show that: the PKC1 gene elicits a distinct spectrum of genetic interactions to SLT2, encoding a non-essential component of the very same signaling pathway. We also show that the terminal phenotype underlying the synthetic lethal network around PKC1 is not uniform. Synthetic lethal genetic networks thus appear to be very heterogeneous in nature with important implications for what functional relationships can be discovered from them.